Are physical inactivity, sitting time and screen time associated with obstructive sleep apnea in adults? A cross-sectional study

Abstract BACKGROUND: Sitting time, screen time and low physical activity (PA) levels have been associated with several diseases and all-cause mortality. PA is related to better sleep quality and absence of daytime sleepiness, along with lower risks of obstructive syndrome apnea (OSA). However, studies on the relationship between sitting time, screen time and OSA are scarce in the literature. OBJECTIVE: To analyze associations between PA levels, sitting time, screen time and OSA among adults with suspected sleep disorder. DESIGN AND SETTING: Cross-sectional study conducted at Hospital Israelita Albert Einstein. METHODS: Data were collected from 369 adults with suspected sleep disorders who visited the hospital’s neurophysiology clinic between August 2015 and January 2017. RESULTS: Correlations between hypopnea and PA indicators were demonstrated for total sitting time (0.123; P = 0.019) and total screen time (0.108; P = 0.038). There was also a correlation between latency for rapid-eye-movement sleep (REM_LAT) and total sitting time (0.103; P = 0.047) and a negative correlation between mean oxyhemoglobin saturation (SaO_Avg) and total PA time (-0.103; P = 0.048). There were no associations between PA parameters and apnea-hypopnea index. After adjusting for confounding factors (body mass index, age and gender), sitting time and screen time were not associated with OSA. CONCLUSION: After adjusting for anthropometric and clinical factors, excessive sitting time or screen time was not associated with OSA in adults suspected of sleep disorders. Age, gender, hypertension, body mass index and waist circumference were associated with OSA.


INTRODUCTION
Obstructive syndrome apnea (OSA) is characterized by repetitive collapse of the upper airways during sleep and is defined by an apnea-hypopnea index (AHI) ≥ 15 events / hour, with reduced airflow, oxygen desaturation and sleep interruption. 1 OSA is associated with chronic diseases such as hypertension, metabolic comorbidities and increased all-cause mortality. [2][3][4][5] The potential serious adverse consequences of untreated OSA are a major reason for emphasizing early diagnosis and treatment. 6,7 The main risk factors for OSA are obesity, age, cranial issues and gender (men). 6,8,9 Moreover physical activity (PA) levels and sedentary behavior (SED, characterized as sitting time, screen time and low energy expenditure) 10 have been identified as new risk factors for most chronic diseases, such as cardiovascular disease, diabetes and some cancers. [11][12][13] There is evidence demonstrating associations between these risk factors and OSA. 14 Although previous studies have explored the association between OSA, PA and SED, no study has analyzed these associations using overnight polysomnography (PSG) as a method for diagnosing OSA.

OBJECTIVE
The purpose of this study was to analyze associations between PA levels, SED and OSA among adults with suspected sleep disorders that were diagnosed at a neurophysiology laboratory for OSA.

METHODS
This study had a cross-sectional design.  July 15, 2015). Subsequently, individuals who visited the neurophysiology clinic at this hospital between August 2015 and January 2017 were invited to participate and signed a written consent statement in order to participate. The following inclusion criteria for participation were adopted: age over 18 years and indication for undergoing overnight laboratory PSG due to suspected OSA. Patients were excluded if they were already receiving treatment for OSA, had a disease that would make it impossible to complete the questionnaires or had technical problems during the overnight laboratory PSG.

Overnight laboratory polysomnography
All individuals participated in an overnight laboratory PSG as previously described. [15][16][17] Individuals were prepared between Medicine Manual. 10 Apnea events were defined as ≥ 90% airflow reduction for ≥ 10 seconds. Hypopnea events were defined as ≥ 30% airflow reduction in association with ≥ 3% drop in oxy- Blood pressure was measured in accordance with international standards, using an aneroid sphygmomanometer with a Dormed pedestal (Dormed, Belo Horizonte, Minas Gerais, Brazil). In the supine position, the arms were kept alongside the body, one of them with a slightly abducted cuff; for patients with an extremely developed thorax, cushions were used to secure the arm at the level of the heart. HTN was considered present when the blood pressure was ≥ 140/90 mmHg. In addition, patients with a history of HTN and patients who were using antihypertensive medications were considered to have HTN.

Statistical analyses
Multinominal models were constructed for each explanatory variable and for each outcome. The odds ratio (OR) was calculated with the respective confidence interval and P-value.  25 and the significance level used was 5%. The correlation between PA and SED indicators (total physical activity time, sitting time and screen time) and sleep indicators from PSG are shown in Table 2. The correlation coefficients obtained indicated that there was a low correlation between REM_ LAT and total sitting time (r = 0.103; P = 0.047) and a low negative correlation between SaO_Avg and total PA time (r = -0.103; P = 0.048). In addition, no correlations were found between AHI and total physical activity time (r = 0.084, P = 0.107), total sitting time (r = 0.067, P = 0.202) or total screen time (r = 0.036, P = 0.485).

Comparisons of sleep indicators obtained from PSG in relation
to PA level and screen time are shown in Table 3. For this evaluation, four groups were created, combining the levels of PA: active (≥ 150 minutes) or inactive (< 150 minutes) with short screen time (< 6 hours and < 10 hours) or long screen time (≥ 6 hours and ≥ 10 hours). We observed differences between the groups in terms of percentage REM sleep (%REM; P = 0.030) and awakening from sleep (SLEEP_WAKE, P = 0.028). In relation to %REM, the average for the inactive group with short screen time was lower than the averages for the inactive group with long screen time (P = 0.019) and the active group with long screen time (P = 0.032). In relation to SLEEP_WAKE, the average for the inactive group with short screen time was higher than the average for the active group with short screen time (P = 0.044).
Moreover, we did not see any evidence of differences between PA indicators in PSG measurements (P > 0.05 in all comparisons). Table 4 shows the comparisons between sleep indicators obtained from PSG in relation to the levels of PA and sitting time using the same combinations of screen time. Using the 6-hour cut-off time it was not possible to adjust the models to compare these groups.

Sitting time and physical activity
In the adjusted models for comparing the PA and sitting time group, considering the 10-hour cutoff, no evidence of differences in PSG measurements (P > 0.05 in all comparisons) were found between the groups.

Logistic regression analysis: sitting time and screen time.
Multiple-approach logistic regression models were built in order to explain physical inactivity and sitting time ( We found no evidence of association between physical inactivity and sleep quality (P > 0.05 in all analyses).      Table 5. Logistic regression model, evaluating the association between physical activity practice and sleep quality, controlled for age, gender, body mass index (BMI) and sitting time (6-hour and 10-hour cutoffs) OR = odds ratio; CI = confidence interval; SD = standard deviation; n = number; AHI = apnea-hypopnea index (events per hour).  Table 6. Logistic regression model. evaluating the association between physical activity practice and sleep quality, controlled for age, gender, body mass index (BMI) and screen time (6-hour and 10-hour cutoffs). OR = odds ratio; CI = confidence interval; SD = standard deviation; n = number; AHI = apnea-hypopnea index (events per hour).

DISCUSSION
Our study showed a correlation between sleep quality and SED and was concordant with the findings from a previous study. 14 In this study, we used PSG indicators to outline the results.
We identified correlations between sitting time and hypopnea and between screen time and hypopnea. In addition, we observed a negative correlation between average oxygen saturation and total physical activity time.
Further to this, we identified that individuals with suspected sleep disorders who were inactive and had short screen time (< 6 hours) had a lower percentage of REM sleep than did individuals with suspected sleep disorders who were inactive and had long screen time (≥ 6). We also showed that awakening from sleep occurred more among individuals with suspected sleep disorders who were inactive and had short screen time (< 6 hours) than among individuals with suspected sleep disorders who were active and had long screen time (≥ 6 hours). This had also been demonstrated in a previous study. 14 Therefore, screen time may be associated with a decrease in sleep quality.
The main finding of our study was that after adjusting for anthropometric and clinical factors, SED analyzed in terms of sitting time and screen time was not associated with sleep quality.
Previous studies also suggested that OSA was affected by age, because the prevalence of OSA increased up to the age of 65 years, at which point, for unclear reasons, the prevalence reached a threshold. Previous data also suggested the interaction between body weight, BMI and OSA in elderly people may be different to that of young adults. Therefore, obesity predisposes and potentiates OSA. 26 In this regard, the prevalence among obese or severely obese patients is almost twice that of normal obese adults. In addition, patients with moderate OSA who gain 10% of their baseline weight present a sixfold increased risk of OSA progression. However, individuals who reduce the same percentage of weight can present an improvement of 20% in the severity of OSA. 26 It is possible that obesity may worsen OSA due to fat deposition at specific sites. Deposition of fat in the tissues surrounding the upper airways seems to result in a lower lumen and greater collapsibility of the upper airways, thus predisposing to apnea. 27,28 In addition, fat deposits around the thorax (truncal obesity) reduce thoracic complacency and functional residual capacity and may increase the demand for oxygen. 29 In this sense, visceral obesity is also considered to be a risk factor for OSA. However, the relationship between OSA and visceral obesity is complex. Although there is evidence showing obesity, as well as visceral obesity, may predispose to OSA and that weight loss results in OSA improvement, previous studies have suggested that OSA may itself cause weight gain. 30,31 Some anthropometric indices, including waist circumference, are widely used as markers for obesity or central obesity. 32 In a recent study, a 1 cm increase in waist circumference gave rise to an 11% increase in the risk of development of OSA. 33 The prevalence of OSA varies according to gender: it is approximately 30% in men and 15% in women. Mechanisms that potentially explain gender differences in the prevalence and severity of OSA include significant variation in body fat distribution, upper airway collapsibility, hormonal status and ventilatory control. [34][35][36] In addition, OSA is a recognized cause of secondary hypertension. 37 Episodes of OSA impose multiple injury; however, intermittent hypoxia (rather than hypercapnia, sleep disruptions or intrathoracic pressure oscillations) is thought to be the most important prohypertensive. 37 Although the mechanisms underlying OSArelated hypertension are not fully understood, the current concept suggests that the sympathetic nervous system and the renin-angiotensin system alter vascular function and structure, resulting in blood pressure elevation. Sympathetic nervous system activity during sleep and wakefulness is heightened in patients with OSA.
The mechanisms that sustain sympathetic activation after withdrawal of chemical stimuli are not known; however, it appears that this chronic sympathetic excitation has both reflex and cen- Our study used the gold standard method to assess OSA, which therefore strengthens the findings obtained.
In relation to PA levels, experimental and intervention studies support the notion that there is a bi-directional relationship between sleep and PA. 38 However, these studies do not necessarily provide insight into sleep and PA patterns. From a clinical standpoint there is growing evidence that aerobic exercise training could be beneficial for adults with a diagnosed sleep disorder. 39 Results from previous studies have indicated that men and women with low PA levels have the highest odds of OSA. 40 Furthermore, there seems to be an inverse relationship between PA level and OSA severity. 41 Reduced PA is associated with increased OSA severity, independent of gender, age and BMI. 41,42 These findings also indicate that efforts to prevent OSA should include encouraging patients to engage in at least some form of moderate-to-vigorous PA. 43 In addition, findings regarding PA may be different among patients with a wide distribution of OSA severity. The reasons for limiting exercise among patients with OSA are unclear.
Some potential contributing factors comprise dyspnea, muscle weakness in the lower limbs, cardiac dysfunction, respiratory muscle dysfunction, arterial hypoxemia, demotivation and peripheral vascular diseases. 39,44 The strengths of the current study included, first, its use of PSG for diagnosing OSA. Second, it used total sitting time and total screen time per week and on weekends. Third, it incorporated information on BMI, age, gender, waist circumference, AH presence and PA level, which allowed us to limit the effects of confounding variables. It is possible, however, that additional confounding effects may have been present.
The limitations of the current study included, first, the cross-sectional nature of this study, which limited the effects of causal inferences. A second limitation related to the sample size, which meant that only a specific population could be analyzed. Lastly, other limitations related to the administration of self-reported measurements of PA and SED levels, and lack of investigation of the specific periods during which individuals remained in front of screens. In a general manner, the present study may serve to generate hypotheses for future research. Future studies should longitudinally investigate the associations between sitting time, screen time, PA and sleep.

CONCLUSIONS
We did not identify any relationship between screen time and sitting time and OSA among adults with suspected sleep disorders, after adjusting for anthropometric and clinical factors.